Scale matching based remote sensing image cloud detection in southwest mountainous areas

Lulu Dong; Yu Chen; Nan Ke; Wenli Tu; Xin Zhang; Wen Dong; Xiaojie Su

doi:10.1117/12.2655170

2 November 2022 Scale matching based remote sensing image cloud detection in southwest mountainous areas

Lulu Dong, Yu Chen, Nan Ke, Wenli Tu, Xin Zhang, Wen Dong, Xiaojie Su

Proceedings Volume 12455, International Conference on Signal Processing and Communication Security (ICSPCS 2022); 124550F (2022) https://doi.org/10.1117/12.2655170
Event: International Conference on Signal Processing and Communication Security (ICSPCS 2022), 2022, Dalian, China

Abstract

Sample quality is the key to automated cloud detection from regional remote sensing images, and scale is one of the major impediments to sample quality control. In this paper, we select the southwest mountainous area in China, which is fragmented, cloudy, and rainy, as the study area. We proposed a method for constructing a cloud detection dataset based on the idea of downscaling and the spectral characteristics of vegetation. Finally, we validated the dataset by the U-Net+ deep learning model. The experimental results show that the cloud detection accuracy reaches 95.11% when using the dataset constructed in this paper, which is approximately 40% higher than the cloud detection accuracy with large-scale samples. Additionally, it reduced the workload of masking a large number of samples for a specific region and realizing the possibility of efficient cloud detection in the region.

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Lulu Dong, Yu Chen, Nan Ke, Wenli Tu, Xin Zhang, Wen Dong, and Xiaojie Su "Scale matching based remote sensing image cloud detection in southwest mountainous areas", Proc. SPIE 12455, International Conference on Signal Processing and Communication Security (ICSPCS 2022), 124550F (2 November 2022); https://doi.org/10.1117/12.2655170

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KEYWORDS

Clouds

Remote sensing

Earth observing sensors

Agriculture

Landsat

Data modeling

Detection and tracking algorithms

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